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Navigating the Depths. Effective Classification of Imbalanced Plankton Classes
Showkat Ahmad
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Naturwissenschaften, Medizin, Informatik, Technik / Sonstiges
Beschreibung
Professorial Dissertation from the year 2024 in the subject Computer Science - Miscellaneous, , language: English, abstract: Plankton is a biotic component at the base of an ecological pyramid and plays an undeniably crucial role in ocean ecosystems and their interconnected environmental dynamics ranging from sustaining marine food webs to influencing the global carbon cycle. Plankton, the collective term encompassing aquatic organisms transported by tides and currents, holds vital insights into these ecosystems. Understanding intricate relationships, distribution patterns, demographic cycles, and their implications for marine food webs and global climate change necessitates detecting, classifying, and monitoring plankton taxa in their ecosystem using specialized imaging devices to collect microscopic samples year-round. Leveraging modern technologies such as machine learning and computer vision, researchers have begun analyzing plankton diversity and abundance. However, the real-world plankton data gathered during monitoring follows an exponential distribution pattern. This non-uniform, class-imbalanced distribution pattern in datasets (including WHOI and NDSB) poses a formidable challenge for classification tasks, especially in identifying rare classes. While a few good attempts have been made to automate the classification of these plankton categories, significant hurdles remain. To address this challenge, we present a novel and systematic approach designed to handle exponentially distributed datasets (specifically WHOI and NDSB) with non-uniform class samples for plankton classification. Departing from traditional methods like resampling and synthetic data generation, we introduce a two-stage complexity-mitigating treatment: Dataset Class Imbalance Treatment (DIT) and Dataset Class-Overlap Treatment (DOT). In the DIT stage, we judiciously prune imbalanced classes based on exclusion criteria we formulated with Ir, and in the DOT stage, we employ our proposed M2 measure to prune classes with overlaps. We then develop the model using this refined dataset for classification. For this purpose, we incorporate a tailored knowledge transfer strategy that involves training and fine-tuning the ResNet Model hyperparameters with an optimizer equipped with a customized cyclic learning rate (CLR) schedule or policy. This strategy enhances our classifier’s ability to grasp new and learned knowledge, producing appreciable outcomes. The results we are achieving are remarkable. [...]
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DIT, CLR, Imbalance-gap, DOT